Short-term Electric Load Forecasting Based on CEEMDAN and LSSVM Optimized by Cuckoo Search AlgorithmFeng Jiang1, Yunfei Zhang21. School of Automation, Huazhong University of Science and Technology, Wuhan 430073, ChinaE-mail: fjiang78@163.com2. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, ChinaE-mail: zyf.2960511894@outlook.com Abstract: The article takes Irish short-term electric load forecasting (STLF) as the research object. Firstly, it uses the adaptive white noise (CEEMDAN) to integrate the empirical mode decomposition to decompose the short-term electric load data, and uses Lempel-Ziv complexity analysis to divide the Intrinsic Mode Function (IMF) obtained after decomposition into three categories: high frequency sequence (HF), the low frequency sequence (LF) and the trend term (T). Then, the least squares support vector machine model (LSSVM) optimized by the cuckoo search algorithm (CS) is utilized to predict it. Finally, the final prediction value is obtained by the add integration method. At the same time, the other five benchmark models are added as the comparison model, and the validity of the model is illustrated by two dimensions: error analysis and model test. The results show that the hybrid integrated model proposed has higher horizontal precision and direction accuracy than other benchmark models, and passes the DM test with the benchmark model. It also demonstrates that the accuracy of the model after adding the decomposition is higher than that of the model without decomposition.Key Words: Short-term Load Forecasting, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Cuckoo Search Algorithm, Least Squares Support Vector Machine1Introduc...
